METHODS AND ANALYSIS: We will identify observational studies through comprehensive literature searches. We will search: MEDLINE, Cochrane Central Register of Controlled Trials for published studies and trial registries including the WHO International Trial Registry Platform and ClinicalTrials.gov. Two reviewers will independently screen the titles and abstracts, attain full text of eligible articles, extract data, and appraise the quality and bias of the included studies. Disagreement among the authors will be resolved by discussion leading to a consensus. Next, we will perform a narrative synthesis of the study results. Study heterogeneity will be assessed using I2 statistics. If I2 is high (≥75%), and plausible heterogeneity contributors are found, we will divide the studies into appropriate subgroups for pooling of results or assess the association of plausible covariates and the prevalence estimates using meta-regression. If I2<75%, we will undertake meta-analysis using the random-effects model and transform all prevalence estimates using the Freeman-Tukey transformation for pooling, to obtain a synthesised point estimate of prevalence with its 95% confidence. We will then back-transform the point estimate, and report our results using the back-transformed figures.
ETHICS AND DISSEMINATION: Ethics approval is not a requirement as this study is based on available published data. Results of this systematic review will be presented at conferences, shared with relevant health authorities, and published in a peer-reviewed journal. These results may help quantify the magnitude of dyslipidaemia globally, and guide preventative and therapeutic interventions.
PROSPERO REGISTRATION NUMBER: CRD42020200281.
METHODS: This is a cross-sectional analysis from the baseline recruitment (years 2007 to 2011) of an ongoing prospective study involving 11,288 participants from 40 rural and urban communities in Malaysia. Multiple logistic regression was used to identify characteristics associated with LLM use.
RESULTS: Majority (74.2%) of participants with CVD were not on LLM. Only 10.5% of participants with high FRS-CVD score, and 17.1% with diabetes were on LLM. Participants who were obese (OR = 1.80, 95% CI: 1.15-2.83), have diabetes (OR = 2.38, 95% CI: 1.78-3.19), have hypertension (OR = 2.87, 95% CI: 2.09-3.95), and attained tertiary education (OR = 2.25, 95% CI: 1.06-4.78) were more likely to be on LLM. Rural residents had lower odds of being on LLM (OR = 0.58, 95% CI: 0.41-0.82). In the primary prevention group, participants with high FRS-CVD score (OR = 3.81, 95% CI: 2.78-5.23) and high-income earners (OR = 1.54, 95% CI: 1.06-2.24) had higher odds of being on LLM.
CONCLUSIONS: LLM use among high CVD-risk individuals in the primary prevention group, and also among individuals with existing CVD was low. While CVD risk factors and global cardiovascular risk score were positively associated with LLM use, sociodemographic disparities were observed among the less-educated, rural residents and low-income earners. Measures are needed to ensure optimal and equitable use of LLM.
METHODS: This cross-sectional study included 390 participants from a primary care clinic in Selangor, Malaysia, between February and June 2022. The inclusion criteria were high-CV risk individuals, that is, Framingham risk score >20%, diabetes without target organ damage, stage 3 kidney disease, and very high levels of low-density lipoprotein cholesterol (LDL-C) >4.9 mmol/L or blood pressure (BP) >180/110 mmHg. Individuals with existing CVD were excluded. The treatment targets were BP <140/90 mmHg (≤135/75 for diabetics), LDL-C <2.6 mmol/L, and HbA1c ≤6.5%. Multiple logistic regressions determined the association between sociodemographic, clinical characteristics, health literacy, and medication adherence with the achievements of each target.
RESULTS: About 7.2% achieved all treatment targets. Of these, 35.1% reached systolic and diastolic (46.7%) BP targets. About 60.2% and 28.2% achieved optimal LDL-C and HbA1c, respectively. Working participants had lower odds of having optimal systolic (aOR = 0.34, 95% CI: 0.13-0.90) and diastolic (aOR = 0.41, 95% CI: 0.17-0.96) BP. Those who adhered to treatments were more likely to achieve LDL-C and HbA1c targets; (aOR = 1.72, 95% CI: 1.10-2.69) and (aOR = 2.46, 95% CI: 1.25-4.83), respectively.
CONCLUSIONS: The control of risk factors among high CV risk patients in this study was suboptimal. Urgent measures such as improving medication adherence are warranted.
CASE SUMMARY: This is the case of a 54-year-old Malay woman with genetically confirmed FH complicated by premature coronary artery disease (PCAD). She was clinically diagnosed in primary care at 52 years old, fulfilling the Simon Broome Criteria (possible FH), Dutch Lipid Clinic Criteria (score of 8: probable FH), and Familial Hypercholesterolaemia Case Ascertainment Tool (relative risk score of 9.51). Subsequently, she was confirmed to have a heterozygous LDLR c.190+4A>T intron 2 pathogenic variant at the age of 53 years. She was known to have hypercholesterolaemia and was treated with statin since the age of 25. However, the lipid-lowering agent was not intensified to achieve the recommended treatment target. The delayed FH diagnosis has caused this patient to have PCAD and percutaneous coronary intervention (PCI) at the age of 29 years and a second PCI at the age of 49 years. She also has a very strong family history of hypercholesterolaemia and PCAD, where seven out of eight of her siblings were affected. Despite this, FH was not diagnosed early, and cascade screening of family members was not conducted, resulting in a missed opportunity to prevent PCAD.
DISCUSSION: Familial hypercholesterolaemia can be clinically diagnosed in primary care to identify those who may require genetic testing. Multidisciplinary care focuses on improving identification, cascade screening, and management of FH, which is vital to improving prognosis and ultimately preventing PCAD.